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(NSW representatives instead wanted more generalist medical positions such as hospitalists). The report notes that those with direct experience of PAs or PA students are confident about the safety and acceptability of PAs for the Australian health system. They also say that PAs would improve the productivity of other health professions, and would be unlikely to threaten the training of medical graduates or the advanced practice roles in other professions. Despite a decade of discussion and two successful pilot programs, says the report, there remains a high level of misunderstanding about the clinical role and professional attributes of PAs and how they might complement and add value to existing team structures. On a related note, the report says: Those who openly declared their opposition to introducing PAs in Australia were likely to advocate for the interests of existing professions, either nursing or medicine. (Croakey wonders if this gives any hint of the reason for the NSW resistance: are the medical and nursing lobbies more influential in NSW?) The report also notes the potential of PAs to reduce health care costs by providing a new workforce group to provide safe and effective services at lower cost. The report, considered by the Australian Health Ministers Advisory Council (AHMAC) in February, has been keenly awaited by PAs and their supporters, including one of the first PAs to graduate in Australia,Ben Stock, who writes below that action is now needed. *** Report represents overwhelming support for PAs Ben Stock writes: In 2011, Health Workforce Australia commissioned a report into the Physician Assistant and their potential role in the Australian health workforce. This report was completed in November 2011 and earlier this year was tabled to the Australian Health Ministers Advisory Council for consideration and it has now just been formally released. This comprehensive report conducted an extensive literature review of supporting documentation regarding Physician Assistants from overseas evidence and considered the impact of the two Australian Physician Assistant trials, which were conducted in Queensland and in South Australia. In addition the report also considered submissions from various key stakeholders such as personnel from the rule and remote health sector, Physician Assistant graduates and students from the Australian PA programs conducted by University of Queensland and other professional bodies representing nurses and doctors. The findings of this report are overwhelmingly supportive of the introduction of the Physician Assistant into the Australian health workforce.

At each scale, Pearsons correlation coefficients were calculated between headcounts and between FWE/FTE across datasets. Correlations between the AIHW survey and the other datasets at the SLA scale were calculated for only those SLAs for which the AIHW survey was not missing information. Thus the correlations exclude information from the Northern Territory. At each scale, correlations between the datasets were also calculated within ASGC remoteness categories. Data from the PHCRIS survey are at the DGP scale which encompass multiple remoteness categories and are excluded from the within-ASGC category correlation analysis. DGPs also occasionally cross state boundaries. To calculate FWE and headcount sums within states, DGPs need to nest in them. To achieve this, DGPs were decomposed to their component SLAs, and SLAs that crossed state boundaries were discarded. Table 2 summarizes the correlation analyses that were implemented. Since the GP headcounts and FTE/FWEs are spatially autocorrelated, traditional metrics of confidence and p-values would be biased. One measure of spatial autocorrelation is Morans I, which ranges from 1 (indicating perfect negative correlation between neighbors), 0 (absence of correlation) to +1 (perfect correlation between neighbors) [ 45 ]. At the SLA scale Morans I is 0.33 (95% CI: 0.33, 0.34) for the AMPCo doctor list FTEs, 0.33 (95%CI: 0.32, 0.34) for AmpCo headcounts and 0.34 (95%CI: 0.34, 0.35) for indirectly derived FWEs. Efrons bootstrap is one approach to estimating confidence intervals in data that are correlated, have outliers, and/or violate other distributional assumptions [ 46 ].